How to write an RFP for agentic AI without buying a promise
Specify the workflow, evidence, responsibility and exit conditions suppliers must meet, then evaluate them through representative scenarios.
Product, procurement and economic disciplines for choosing and operating agentic AI responsibly.
Specify the workflow, evidence, responsibility and exit conditions suppliers must meet, then evaluate them through representative scenarios.
Model concrete failure scenarios across detection, containment, correction, disruption and consequence, then use ranges to choose controls.
Accept an agent against outcome distributions, critical invariants and recovery evidence—not one scripted response or an average score.
Model usage is one line in a wider cost stack that includes integration, evaluation, control, human work, incidents, change and exit.
Give decision owners a balanced view of outcomes, quality, human work, cost, risk and change—with every measure tied to an action.
Connect spending to accepted workflow outcomes by counting retries, review, correction and failures alongside model and platform charges.
Choose a sourcing path by separating the workflow advantage you must own from the capabilities you can responsibly obtain elsewhere.
Time saved is not an outcome until the original work, quality, case mix and destination of released capacity are measured on comparable terms.
Keep workflow cases, graders and decision rules separate from the platform so alternatives can be compared without rebuilding the standard of proof.
A demonstration shows one possible path. A buying decision needs evidence about representative work, failure, authority, recovery and operation.
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Read the method, assumptions and questions to ask.